In the past few years, numerous works have proposed to deal with Continual Learning and avoid catastrophic forgetting. They can be divided into three families [1]}: regularisation-based methods, exemplar-based methods, and bias-correction methods. Regularisation-based approaches minimize the impact of learning on the weights that are important for previous tasks. Among them, EWC [2]} calculates the weights of model parameters with a Fisher Information Matrix, while PathInt [3]} accumulates changes in each parameter during the learning process. RWalk [4]} uses both
approaches complemented with exemplars to improve results. Other approaches use knowledge distillation [5]} such as LwF [6]} which trains a current model (student) with a past model as a teacher to prevent drifting while learning new tasks. The student model can also be guided using the attention of the teacher model as in LwM [7]}.
